scholarly journals Serotonergic Psychedelics LSD & Psilocybin Increase the Fractal Dimension of Cortical Brain Activity in Spatial and Temporal Domains

2019 ◽  
Author(s):  
TF Varley ◽  
R Carhart-Harris ◽  
Leor Roseman ◽  
David K Menon ◽  
EA Stamatakis
Keyword(s):  
Fractal Dimension ◽  
Functional Connectivity ◽  
Critical Point ◽  
Fractal Structure ◽  
Brain Activity ◽  
Past Research ◽  
Psychedelic Drugs ◽  
Thought Patterns ◽  
The Mind ◽  
The Brain

AbstractPsychedelic drugs, such as psilocybin and LSD, represent unique tools for researchers in-vestigating the neural origins of consciousness. Currently, the most compelling theories of how psychedelics exert their effects is by increasing the complexity of brain activity and moving the system towards a critical point between order and disorder, creating more dynamic and complex patterns of neural activity. While the concept of criticality is of central importance to this theory, few of the published studies on psychedelics investigate it directly, testing instead related measures such as algorithmic complexity or Shannon entropy. We propose using the fractal dimension of functional activity in the brain as a measure of complexity since findings from physics suggest that as a system organizes towards criticality, it tends to take on a fractal structure. We tested two different measures of fractal dimension, one spatial and one temporal, using fMRI data from volunteers under the influence of both LSD and psilocybin. The first was the fractal dimension of cortical functional connectivity networks and the second was the fractal dimension of BOLD time-series. We were able to show that both psychedelic drugs significantly increased the fractal dimension of functional connectivity networks, and that LSD significantly increased the fractal dimension of BOLD signals, with psilocybin showing a non-significant trend in the same direction. With both LSD and psilocybin, we were able to localize changes in the fractal dimension of BOLD signals to brain areas assigned to the dorsal-attentional network. These results show that psychedelic drugs increase the fractal character of activity in the brain and we see this as an indicator that the changes in consciousness triggered by psychedelics are associated with evolution towards a critical zone.Author SummaryThe unique state of consciousness produced by psychedelic drugs like LSD and psilocybin (the active component in magic mushrooms) are potentially useful tools for discovering how specific changes in the brain are related to differences in perception and thought patterns. Past research into the neuroscience of psychedelics has led to the proposal of a general theory of brain function and consciousness: the Entropic Brain Hypothesis proposes that consciousness emerges when the brain is sitting near a critical tipping point between order and chaos and that the mind-expanding elements of the psychedelic experience are caused by the brain moving closer to that critical transition point. Physicists have discovered that near this critical point, many different kinds of systems, from magnets to ecosystems, take on a distinct, fractal structure. Here, we used two measures of fractal-quality of brain activity, as seen in fMRI, to test whether the activity of the brain on psychedelics is more fractal than normal. We found evidence that this is the case and interpret that as supporting the theory that, psychedelic drugs are move the brain towards a more critical state.

scholarly journals Fractal Dimension of Cortical Functional Connectivity Networks Predicts Severity in Disorders of Consciousness

2019 ◽  
Author(s):  
TF. Varley ◽  
M. Craig ◽  
R. Adapa ◽  
P. Finoia ◽  
G. Williams ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Functional Connectivity ◽  
Healthy Volunteers ◽  
Critical Point ◽  
Brain Activity ◽  
Conscious Experience ◽  
Minimally Conscious State ◽  
Disorders Of Consciousness ◽  
Level Of Consciousness ◽  
The Brain

AbstractRecent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain, and that consciousness emerges in a critical zone on the axes of order/randomness and integration/differentiation. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested the several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n=15), patients in a minimally conscious state (n=10), and patients in a vegetative state (n=8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is predictive of level of consciousness in a clinically relevant population, with more fractal (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

2020 ◽  
pp. 1-21
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Right Hemisphere ◽  
Random Network ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

Mind, identity theory of

2018 ◽  
Author(s):  
Frank Jackson
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Identity Theory ◽  
Brain Activity ◽  
Modern Science ◽  
Mental States ◽  
Ontological Categories ◽  
Great Merit ◽  
The Mind ◽  
The Brain

We know that the brain is intimately connected with mental activity. Indeed, doctors now define death in terms of the cessation of the relevant brain activity. The identity theory of mind holds that the intimate connection is identity: the mind is the brain, or, more precisely, mental states are states of the brain. The theory goes directly against a long tradition according to which mental and material belong to quite distinct ontological categories – the mental being essentially conscious, the material essentially unconscious. This tradition has been bedevilled by the problem of how essentially immaterial states could be caused by the material world, as would happen when we see a tree, and how they could cause material states, as would happen when we decide to make an omelette. A great merit of the identity theory is that it avoids this problem: interaction between mental and material becomes simply interaction between one subset of material states, namely certain states of a sophisticated central nervous system, and other material states. The theory also brings the mind within the scope of modern science. More and more phenomena are turning out to be explicable in the physical terms of modern science: phenomena once explained in terms of spells, possession by devils, Thor’s thunderbolts, and so on, are now explained in more mundane, physical terms. If the identity theory is right, the same goes for the mind. Neuroscience will in time reveal the secrets of the mind in the same general way that the theory of electricity reveals the secrets of lightning. This possibility has received enormous support from advances in computing. We now have at least the glimmerings of an idea of how a purely material or physical system could do some of the things minds can do. Nevertheless, there are many questions to be asked of the identity theory. How could states that seem so different turn out to be one and the same? Would neurophysiologists actually see my thoughts and feelings if they looked at my brain? When we report on our mental states what are we reporting on – our brains?

scholarly journals Fractal Dimension as Quantifier of EEG Activity in Driving Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1311
Author(s):  
Mª Victoria Sebastián ◽  
Mª Antonia Navascués ◽  
Antonio Otal ◽  
Carlos Ruiz ◽  
Mª Ángeles Idiazábal ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Brain Activity ◽  
Fractal Theory ◽  
Driving Simulation ◽  
Geometric Complexity ◽  
Eeg Activity ◽  
Modeling And Analysis ◽  
Brain Waves ◽  
Electroencephalographic Signals ◽  
The Brain

Dynamical systems and fractal theory methodologies have been proved useful for the modeling and analysis of experimental datasets and, in particular, for electroencephalographic signals. The computation of the fractal dimension of approximation curves in the plane enables the assignment of numerical values to bioelectric recordings in order to discriminate between different states of the observed system. The procedure does not require the stationarity of the signals nor extremely long segments of data. In previous works, we checked that this parameter is a good index for brain activity. In this paper, we consider this measurement in order to quantify the geometric complexity of the brain waves in states of rest and during vehicle driving simulation in different scenarios. This work presents evidence that the fractal dimension allows the detection of the brain bioelectric changes produced in the areas that carry out the different driving simulation tasks, increasing with their complexity.

Aquinas on Consciousness and the Human Soul

2020 ◽  
Vol 45 (12) ◽  
pp. 3-4
Author(s):  
Edward J. Furton ◽  
Keyword(s):  
Brain Activity ◽  
Thomas Aquinas ◽  
Human Soul ◽  
Brain Dead ◽  
The Mind ◽  
The Brain

The materialistic premise supposes that a patient’s reduced brain activity indicates that the mind is beginning to approach nonexistence. Such persons may not be brain dead, but they have a life that is close enough to death to allow us to treat them with a certain disregard. For the Catholic, this overlooks the enduring presence of the soul and its two spiritual powers of intellect and will. St. Thomas Aquinas is our best guide to exploring the implications of this view for patients in states of diminished consciousness. The externally observable activity of the brain, even when dramatically lessened, does not represent any loss of the powers of the soul, which continue to function through a combination of natural and divine influences.

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

scholarly journals Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

2020 ◽  
Author(s):  
Z. Zavecz ◽  
K. Janacsek ◽  
P. Simor ◽  
M.X. Cohen ◽  
D. Nemeth
Keyword(s):  
Functional Connectivity ◽  
Memory Consolidation ◽  
Brain Activity ◽  
Serial Order ◽  
Order Information ◽  
Novel Approach ◽  
Continuous Eeg ◽  
Induced Changes ◽  
Probability Information ◽  
The Brain

AbstractLong-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation.Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

scholarly journals A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

2021 ◽  
Author(s):  
SUBBA REDDY OOTA ◽  
Archi Yadav ◽  
Arpita Dash ◽  
Surampudi Bapi Raju ◽  
Avinash Sharma
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Reaction Diffusion ◽  
Complex Nature ◽  
Relational Structures ◽  
Human Connectome Project ◽  
The Brain

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of human brain function.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA
Keyword(s):  
Fractal Dimension ◽  
Motor Imagery ◽  
Fractal Analysis ◽  
Brain Activity ◽  
Eeg Signal ◽  
Left Hand ◽  
Right Hand ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

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